1. Field of the Invention
The present invention generally relates to computer-implemented methods for classifying defects that occurred during the fabrication of semiconductor wafers or specimens. Certain embodiments relate to a computer-implemented method that includes flexibly applying one or more sequences of statistical rules, deterministic rules, hybrid rules, or a combination thereof for defects found by inspection of a semiconductor specimen and classifying the defects based on results of the application of the sequence of rules.
2. Description of the Related Art
The following description and examples are not admitted to be prior art by virtue of their inclusion in this section.
Wafer inspection systems often find thousands of anomalies (commonly called “events” or “defects”) on each wafer. Defects may have many forms such as structural flaws, process residues, and external contamination that may occur during semiconductor wafer fabrication. Defect classification is used to make sense of the large amount of information generated by inspection and to locate the “interesting” defects. As processes for making wafers evolve, the defect types of interest change. The importance of a defect depends on several factors including appearance, and other characteristics such as location, proximity to other defects, and prior history of the specimen.
Many different defect classification methods have been used. The existing methods for classifying defects in the field of semiconductor inspection fall into three general categories: 1) deterministic rule based methods, 2) statistical/training based methods, and 3) fixed combinations of deterministic rules and trained characteristics.
Examples of fully rule-based approaches include Run Time Classification (RTC) provided on the AIT II, AIT III, and AIT XP systems, which are commercially available from KLA-Tencor, San Jose, Calif., the early release of on-the-fly (OTF) classification methods on the Compass tools commercially available from Applied Materials Inc., Santa Clara, Calif., and gray level binning for voltage contrast defects, which is commercially available from Hermes MicroVision, Milpitas, Calif. The setup of such a classifier is relatively simple and easy for the user to understand. Many of these approaches provide some user assistance by showing how the defects have been separated through a variety of graphical means and by showing examples of defects in each bin. Deterministic rule-based classifiers generally have a high throughput.
Examples of statistical (trained) classification are the current automatic defect classification (ADC) and inline ADC (iADC) products on the 23xx, AIT, eSxx, and eV300 tools commercially available from KLA-Tencor. These particular examples use a statistical classification (e.g., nearest neighbor) approach to separate defects. An additional example of a trained classifier is the current release of OTF called “OTF Grouping” commercially available from Applied Materials Inc., Santa Clara, Calif. These classification algorithms use a mathematical representation of the defects' appearance and context (sometimes called “defect features”) in a “black box” fashion, matching the defects to a training set, although the user may have control of the importance of low false positive or false negative assessments for each bin.
One example of a hybrid approach is SEMVision ADC, which is commercially available from Applied Materials Inc., Santa Clara, Calif., and which has a fixed set of bins called core classes that are based mainly on defect boundary analysis, segmentation of background, and depth of defect through multi-perspective imaging. While the tree structure, which defines the order and type of decisions to be made, is fixed in this approach, the thresholding for classifying can be set by the user.
Although the above-described methods are modestly successful at defect classification, each of these methods can be improved. For example, many deterministic methods do not include all of the characteristics of the defects that are relevant to good classification. In addition, fixed boundaries often do not work well over time on different specimens. The deterministic rule based methods are also generally inflexible in the usage of rules and defect characteristics. In addition, these methods generally include some restrictions on the number and kinds of characteristics and how they are combined. Furthermore, these methods generally have user interface deficiencies in being able to create the classification recipe. For example, the user interface can be complex to navigate, and the final results may not be clear.
One disadvantage of the fully trained approaches is that these methods generally rely on having a sufficient population of the defects for each bin available for training. These methods also need to be maintained and updated as defects that look different are found or as processing conditions change. In addition, these methods work in a way that may not reflect the intentions of the user because these methods function as a black box (i.e., the user is unable to select the characteristics or characteristic groups to be used to do the classification). Furthermore, these methods often neglect non-appearance characteristics that can be important in separating defects for purposes of analysis. Lastly, fully trained classifiers are generally slower to execute than deterministic rules, particularly ones trained with a large number of characteristics.
The inflexible, hybrid methods have disadvantages such as that these methods often do not account for novel ways that the user might want to separate defects for a particular image or specimen. In addition, these methods rigidly restrict the paths used to bin the defects.
Accordingly, it may be advantageous to develop computer-implemented methods for classifying defects that eliminate one or more of the disadvantages described above.